Quantitative structure-activity relationship (QSAR) models and their applicability domain analysis on HIV-1 protease inhibitors by machine learning methods-Reference-Cited by-同舟云学术

Quantitative structure-activity relationship (QSAR) models and their applicability domain analysis on HIV-1 protease inhibitors by machine learning methods

Published:2020-01 Issue: Volume:196 Page:103888
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Tian Yujia,Zhang Shengde,Yin Hongyan,Yan Aixia

Funder

National Natural Science Foundation of China

Beijing University of Chemical Technology

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference89 articles.

1. HIV-1 protease cleavage site prediction based on two-stage feature selection method;Niu;Protein Pept. Lett.,2013

2. Folding regulates autoprocessing of HIV-1 protease precursor * □;Chatterjee;J. Biol. Chem.,2005

3. HIV protease inhibitors : a review of molecular selectivity and toxicity;Chu;HIV AIDS Res. Palliat. Care,2015

4. HIV-1 protease cleavage site prediction based on amino acid property;Niu;J. Comput. Chem.,2009

5. Recent progress in the development of HIV - 1 protease inhibitors for the treatment of HIV/AIDS;Ghosh;J. Med. Chem.,2016

Cited by 22 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Quantitative Structure–Property Relationship Model for Surface Tension Based on Artificial Neural Network;International Journal of Thermophysics;2024-07

2. Unveiling potent inhibitors for schistosomiasis through ligand-based drug design, molecular docking, molecular dynamics simulations and pharmacokinetics predictions;PLOS ONE;2024-06-26

3. Unveiling Novel HIV-1 Protease Inhibitors through an Integrated Analysis of 3D-QSAR, Molecular Docking, and Binding Free Energy;Letters in Drug Design & Discovery;2024-06-03

4. Discovery of potential protein tyrosine phosphatase non-receptor type 2 inhibitors from phytochemical database through structure- based high-throughput virtual screening and machine learning Discovery of protein tyrosine phosphatase non-receptor type 2 inhibitors;2024-02-05

5. Analysis of good and bad fingerprint for identification of NIR based optical frameworks using Monte Carlo method;Microchemical Journal;2024-01